Overview

Dataset statistics

Number of variables15
Number of observations457
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.7 KiB
Average record size in memory120.3 B

Variable types

Numeric14
Categorical1

Alerts

TotalSteps is highly correlated with TotalDistance and 8 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 8 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 8 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 6 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with TotalSteps and 6 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly correlated with TotalSteps and 2 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 7 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 2 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 4 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly correlated with TotalSteps and 3 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 7 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Id is highly correlated with TotalSteps and 6 other fieldsHigh correlation
ActivityDate is highly correlated with CaloriesHigh correlation
TotalSteps is highly correlated with Id and 9 other fieldsHigh correlation
TotalDistance is highly correlated with Id and 9 other fieldsHigh correlation
TrackerDistance is highly correlated with Id and 9 other fieldsHigh correlation
LoggedActivitiesDistance is highly correlated with SedentaryActiveDistance and 1 other fieldsHigh correlation
VeryActiveDistance is highly correlated with Id and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
SedentaryActiveDistance is highly correlated with LoggedActivitiesDistanceHigh correlation
VeryActiveMinutes is highly correlated with Id and 4 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with ModeratelyActiveDistanceHigh correlation
LightlyActiveMinutes is highly correlated with Id and 7 other fieldsHigh correlation
SedentaryMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
Calories is highly correlated with Id and 6 other fieldsHigh correlation
TotalSteps has 61 (13.3%) zeros Zeros
TotalDistance has 63 (13.8%) zeros Zeros
TrackerDistance has 66 (14.4%) zeros Zeros
LoggedActivitiesDistance has 433 (94.7%) zeros Zeros
VeryActiveDistance has 245 (53.6%) zeros Zeros
ModeratelyActiveDistance has 228 (49.9%) zeros Zeros
LightActiveDistance has 74 (16.2%) zeros Zeros
SedentaryActiveDistance has 419 (91.7%) zeros Zeros
VeryActiveMinutes has 241 (52.7%) zeros Zeros
FairlyActiveMinutes has 227 (49.7%) zeros Zeros
LightlyActiveMinutes has 72 (15.8%) zeros Zeros
Calories has 5 (1.1%) zeros Zeros

Reproduction

Analysis started2023-02-12 07:50:36.736151
Analysis finished2023-02-12 07:51:08.705247
Duration31.97 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct35
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4628594643
Minimum1503960366
Maximum8877689391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:08.865141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1503960366
5-th percentile1624580081
Q12347167796
median4057192912
Q36391747486
95-th percentile8792009665
Maximum8877689391
Range7373729025
Interquartile range (IQR)4044579690

Descriptive statistics

Standard deviation2293781430
Coefficient of variation (CV)0.4955675764
Kurtosis-1.039194515
Mean4628594643
Median Absolute Deviation (MAD)2030840877
Skewness0.3527246238
Sum2.115267752 × 1012
Variance5.261433247 × 1018
MonotonicityIncreasing
2023-02-12T13:21:08.987813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
405719291232
 
7.0%
402033265032
 
7.0%
150396036619
 
4.2%
162458008119
 
4.2%
234716779615
 
3.3%
470292168415
 
3.3%
444511498615
 
3.3%
696218106714
 
3.1%
232012700212
 
2.6%
455860992412
 
2.6%
Other values (25)272
59.5%
ValueCountFrequency (%)
150396036619
4.2%
162458008119
4.2%
164443008110
2.2%
184450507212
2.6%
192797227912
2.6%
202248440812
2.6%
202635203512
2.6%
232012700212
2.6%
234716779615
3.3%
287321276512
2.6%
ValueCountFrequency (%)
887768939112
2.6%
879200966512
2.6%
85838150598
1.8%
837856320012
2.6%
825324287912
2.6%
805347532811
2.4%
708636192612
2.6%
700774417112
2.6%
696218106714
3.1%
67758889559
2.0%

ActivityDate
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
4/2/2016
35 
4/3/2016
35 
4/4/2016
35 
4/5/2016
35 
4/1/2016
34 
Other values (27)
283 

Length

Max length9
Median length8
Mean length8.332603939
Min length8

Characters and Unicode

Total characters3808
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3/25/2016
2nd row3/26/2016
3rd row3/27/2016
4th row3/28/2016
5th row3/29/2016

Common Values

ValueCountFrequency (%)
4/2/201635
 
7.7%
4/3/201635
 
7.7%
4/4/201635
 
7.7%
4/5/201635
 
7.7%
4/1/201634
 
7.4%
4/6/201633
 
7.2%
4/7/201633
 
7.2%
4/8/201633
 
7.2%
4/9/201632
 
7.0%
4/10/201629
 
6.3%
Other values (22)123
26.9%

Length

2023-02-12T13:21:09.108526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4/2/201635
 
7.7%
4/5/201635
 
7.7%
4/3/201635
 
7.7%
4/4/201635
 
7.7%
4/1/201634
 
7.4%
4/6/201633
 
7.2%
4/7/201633
 
7.2%
4/8/201633
 
7.2%
4/9/201632
 
7.0%
4/10/201629
 
6.3%
Other values (22)123
26.9%

Most occurring characters

ValueCountFrequency (%)
/914
24.0%
1623
16.4%
2556
14.6%
0499
13.1%
6496
13.0%
4422
11.1%
3135
 
3.5%
944
 
1.2%
541
 
1.1%
739
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2894
76.0%
Other Punctuation914
 
24.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1623
21.5%
2556
19.2%
0499
17.2%
6496
17.1%
4422
14.6%
3135
 
4.7%
944
 
1.5%
541
 
1.4%
739
 
1.3%
839
 
1.3%
Other Punctuation
ValueCountFrequency (%)
/914
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/914
24.0%
1623
16.4%
2556
14.6%
0499
13.1%
6496
13.0%
4422
11.1%
3135
 
3.5%
944
 
1.2%
541
 
1.1%
739
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/914
24.0%
1623
16.4%
2556
14.6%
0499
13.1%
6496
13.0%
4422
11.1%
3135
 
3.5%
944
 
1.2%
541
 
1.1%
739
 
1.0%

TotalSteps
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct389
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6546.562363
Minimum0
Maximum28497
Zeros61
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:09.222189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11988
median5986
Q310198
95-th percentile15605.6
Maximum28497
Range28497
Interquartile range (IQR)8210

Descriptive statistics

Standard deviation5398.493064
Coefficient of variation (CV)0.824630205
Kurtosis0.6648241126
Mean6546.562363
Median Absolute Deviation (MAD)4120
Skewness0.803413395
Sum2991779
Variance29143727.36
MonotonicityNot monotonic
2023-02-12T13:21:09.359854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
 
13.3%
20982
 
0.4%
82
 
0.4%
55432
 
0.4%
124092
 
0.4%
44992
 
0.4%
63442
 
0.4%
41952
 
0.4%
72
 
0.4%
19881
 
0.2%
Other values (379)379
82.9%
ValueCountFrequency (%)
061
13.3%
41
 
0.2%
72
 
0.4%
82
 
0.4%
141
 
0.2%
181
 
0.2%
201
 
0.2%
241
 
0.2%
441
 
0.2%
871
 
0.2%
ValueCountFrequency (%)
284971
0.2%
275721
0.2%
257011
0.2%
241361
0.2%
230141
0.2%
207791
0.2%
202371
0.2%
201881
0.2%
196581
0.2%
189521
0.2%

TotalDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct334
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.663522972
Minimum0
Maximum27.53000069
Zeros63
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:09.499480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.409999967
median4.090000153
Q37.159999847
95-th percentile11.23999977
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.749999881

Descriptive statistics

Standard deviation4.082072268
Coefficient of variation (CV)0.8753194296
Kurtosis3.448976394
Mean4.663522972
Median Absolute Deviation (MAD)2.889999866
Skewness1.321383526
Sum2131.229998
Variance16.663314
MonotonicityNot monotonic
2023-02-12T13:21:09.627138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
 
13.8%
0.0099999997766
 
1.3%
4.719999793
 
0.7%
7.6700000763
 
0.7%
5.4099998472
 
0.4%
2.1400001052
 
0.4%
3.8399999142
 
0.4%
0.019999999552
 
0.4%
0.25999999052
 
0.4%
1.5499999522
 
0.4%
Other values (324)370
81.0%
ValueCountFrequency (%)
063
13.8%
0.0099999997766
 
1.3%
0.019999999552
 
0.4%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999521
 
0.2%
0.14000000062
 
0.4%
0.18999999761
 
0.2%
0.20999999341
 
0.2%
0.25999999052
 
0.4%
ValueCountFrequency (%)
27.530000691
0.2%
23.389999391
0.2%
20.909999851
0.2%
20.389999391
0.2%
20.139999391
0.2%
18.409999851
0.2%
15.819999691
0.2%
15.619999891
0.2%
14.840000151
0.2%
14.710000041
0.2%

TrackerDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct336
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.609846824
Minimum0
Maximum27.53000069
Zeros66
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:09.764736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.279999971
median4.090000153
Q37.110000134
95-th percentile11.13599968
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.830000162

Descriptive statistics

Standard deviation4.068539937
Coefficient of variation (CV)0.8825759494
Kurtosis3.576888152
Mean4.609846824
Median Absolute Deviation (MAD)2.920000076
Skewness1.339050019
Sum2106.699999
Variance16.55301722
MonotonicityNot monotonic
2023-02-12T13:21:09.892401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
066
 
14.4%
0.0099999997766
 
1.3%
4.719999793
 
0.7%
7.6700000763
 
0.7%
6.7899999622
 
0.4%
6.1500000952
 
0.4%
11.239999772
 
0.4%
1.9199999572
 
0.4%
4.1399998662
 
0.4%
1.4299999482
 
0.4%
Other values (326)367
80.3%
ValueCountFrequency (%)
066
14.4%
0.0099999997766
 
1.3%
0.019999999552
 
0.4%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999521
 
0.2%
0.14000000062
 
0.4%
0.18999999761
 
0.2%
0.20999999341
 
0.2%
0.25999999052
 
0.4%
ValueCountFrequency (%)
27.530000691
0.2%
23.389999391
0.2%
20.909999851
0.2%
20.389999391
0.2%
20.139999391
0.2%
18.409999851
0.2%
15.819999691
0.2%
15.619999891
0.2%
14.840000151
0.2%
14.710000041
0.2%

LoggedActivitiesDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1794273741
Minimum0
Maximum6.72705698
Zeros433
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:10.018068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3665432006
Maximum6.72705698
Range6.72705698
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8492318298
Coefficient of variation (CV)4.733011526
Kurtosis27.13615724
Mean0.1794273741
Median Absolute Deviation (MAD)0
Skewness5.159788142
Sum81.99830997
Variance0.7211947008
MonotonicityNot monotonic
2023-02-12T13:21:10.114810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0433
94.7%
2.0921471124
 
0.9%
2.2530810833
 
0.7%
1.6093440061
 
0.2%
5.1898498541
 
0.2%
3.2186880111
 
0.2%
4.8697829251
 
0.2%
4.8423199651
 
0.2%
4.8759899141
 
0.2%
4.8357200621
 
0.2%
Other values (10)10
 
2.2%
ValueCountFrequency (%)
0433
94.7%
0.055842999371
 
0.2%
1.6093440061
 
0.2%
1.9263019561
 
0.2%
2.0277729031
 
0.2%
2.0921471124
 
0.9%
2.2530810833
 
0.7%
2.6964550021
 
0.2%
3.2186880111
 
0.2%
3.972795011
 
0.2%
ValueCountFrequency (%)
6.727056981
0.2%
5.456863881
0.2%
5.1898498541
0.2%
4.9012827871
0.2%
4.8759899141
0.2%
4.8697829251
0.2%
4.8423199651
0.2%
4.8363800051
0.2%
4.8357200621
0.2%
4.8280320171
0.2%

VeryActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct170
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.180897153
Minimum0
Maximum21.92000008
Zeros245
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:10.238556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.309999943
95-th percentile5.760000038
Maximum21.92000008
Range21.92000008
Interquartile range (IQR)1.309999943

Descriptive statistics

Standard deviation2.487158568
Coefficient of variation (CV)2.106160186
Kurtosis18.7096635
Mean1.180897153
Median Absolute Deviation (MAD)0
Skewness3.73064421
Sum539.669999
Variance6.185957745
MonotonicityNot monotonic
2023-02-12T13:21:10.367154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0245
53.6%
0.07000000036
 
1.3%
0.254
 
0.9%
0.33000001313
 
0.7%
0.23000000423
 
0.7%
0.53
 
0.7%
2.5699999332
 
0.4%
0.079999998212
 
0.4%
0.059999998662
 
0.4%
0.88999998572
 
0.4%
Other values (160)185
40.5%
ValueCountFrequency (%)
0245
53.6%
0.0099999997761
 
0.2%
0.019999999551
 
0.2%
0.039999999111
 
0.2%
0.059999998662
 
0.4%
0.07000000036
 
1.3%
0.079999998212
 
0.4%
0.090000003581
 
0.2%
0.10000000152
 
0.4%
0.10999999941
 
0.2%
ValueCountFrequency (%)
21.920000081
0.2%
16.819999691
0.2%
14.720000271
0.2%
12.220000271
0.2%
12.060000421
0.2%
11.729999541
0.2%
11.100000381
0.2%
9.9799995421
0.2%
9.9700002671
0.2%
9.9600000381
0.2%

ModeratelyActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct140
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4786433252
Minimum0
Maximum6.400000095
Zeros228
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:10.504014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01999999955
Q30.6700000167
95-th percentile2.089999914
Maximum6.400000095
Range6.400000095
Interquartile range (IQR)0.6700000167

Descriptive statistics

Standard deviation0.8309951707
Coefficient of variation (CV)1.736146995
Kurtosis11.87056988
Mean0.4786433252
Median Absolute Deviation (MAD)0.01999999955
Skewness2.971412313
Sum218.7399996
Variance0.6905529737
MonotonicityNot monotonic
2023-02-12T13:21:10.888257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0228
49.9%
0.18000000725
 
1.1%
0.255
 
1.1%
0.33000001314
 
0.9%
0.25999999054
 
0.9%
0.23000000424
 
0.9%
0.37000000484
 
0.9%
0.52999997144
 
0.9%
0.21999999884
 
0.9%
0.79000002154
 
0.9%
Other values (130)191
41.8%
ValueCountFrequency (%)
0228
49.9%
0.019999999551
 
0.2%
0.039999999112
 
0.4%
0.050000000753
 
0.7%
0.059999998661
 
0.2%
0.090000003581
 
0.2%
0.10000000151
 
0.2%
0.11999999732
 
0.4%
0.12999999521
 
0.2%
0.14000000061
 
0.2%
ValueCountFrequency (%)
6.4000000951
0.2%
5.4899997711
0.2%
4.4899997711
0.2%
4.4400000571
0.2%
3.7200000291
0.2%
3.6800000671
0.2%
3.5999999051
0.2%
3.3399999141
0.2%
3.259999991
0.2%
3.1199998861
0.2%

LightActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct295
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.890196937
Minimum0
Maximum12.51000023
Zeros74
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:11.041811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8700000048
median2.930000067
Q34.460000038
95-th percentile6.515999985
Maximum12.51000023
Range12.51000023
Interquartile range (IQR)3.590000033

Descriptive statistics

Standard deviation2.237523344
Coefficient of variation (CV)0.7741767752
Kurtosis0.2750757813
Mean2.890196937
Median Absolute Deviation (MAD)1.749999762
Skewness0.5215831453
Sum1320.82
Variance5.006510715
MonotonicityNot monotonic
2023-02-12T13:21:11.179478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074
 
16.2%
0.0099999997767
 
1.5%
3.54
 
0.9%
4.6100001344
 
0.9%
4.4499998093
 
0.7%
3.2100000383
 
0.7%
3.9100000863
 
0.7%
4.6799998283
 
0.7%
4.4600000383
 
0.7%
5.2699999813
 
0.7%
Other values (285)350
76.6%
ValueCountFrequency (%)
074
16.2%
0.0099999997767
 
1.5%
0.019999999551
 
0.2%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999522
 
0.4%
0.14000000061
 
0.2%
0.17000000181
 
0.2%
0.18999999761
 
0.2%
0.20999999342
 
0.4%
ValueCountFrequency (%)
12.510000231
0.2%
121
0.2%
9.3699998861
0.2%
8.6199998861
0.2%
8.1499996191
0.2%
8.0799999241
0.2%
8.060000421
0.2%
8.0200004581
0.2%
7.7899999621
0.2%
7.5599999431
0.2%

SedentaryActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001903719882
Minimum0
Maximum0.1000000015
Zeros419
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:11.290187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.009999999776
Maximum0.1000000015
Range0.1000000015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0084868013
Coefficient of variation (CV)4.45800949
Kurtosis54.03881246
Mean0.001903719882
Median Absolute Deviation (MAD)0
Skewness6.566301549
Sum0.8699999861
Variance7.202579631 × 10-5
MonotonicityNot monotonic
2023-02-12T13:21:11.380904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0419
91.7%
0.00999999977622
 
4.8%
0.029999999336
 
1.3%
0.039999999114
 
0.9%
0.019999999552
 
0.4%
0.059999998662
 
0.4%
0.10000000151
 
0.2%
0.050000000751
 
0.2%
ValueCountFrequency (%)
0419
91.7%
0.00999999977622
 
4.8%
0.019999999552
 
0.4%
0.029999999336
 
1.3%
0.039999999114
 
0.9%
0.050000000751
 
0.2%
0.059999998662
 
0.4%
0.10000000151
 
0.2%
ValueCountFrequency (%)
0.10000000151
 
0.2%
0.059999998662
 
0.4%
0.050000000751
 
0.2%
0.039999999114
 
0.9%
0.029999999336
 
1.3%
0.019999999552
 
0.4%
0.00999999977622
 
4.8%
0419
91.7%

VeryActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct85
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.62363239
Minimum0
Maximum202
Zeros241
Zeros (%)52.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:11.506568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile78.4
Maximum202
Range202
Interquartile range (IQR)25

Descriptive statistics

Standard deviation28.91970375
Coefficient of variation (CV)1.739674163
Kurtosis6.928585794
Mean16.62363239
Median Absolute Deviation (MAD)0
Skewness2.38473603
Sum7597
Variance836.3492648
MonotonicityNot monotonic
2023-02-12T13:21:11.639215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0241
52.7%
111
 
2.4%
39
 
2.0%
48
 
1.8%
27
 
1.5%
187
 
1.5%
157
 
1.5%
77
 
1.5%
256
 
1.3%
56
 
1.3%
Other values (75)148
32.4%
ValueCountFrequency (%)
0241
52.7%
111
 
2.4%
27
 
1.5%
39
 
2.0%
48
 
1.8%
56
 
1.3%
63
 
0.7%
77
 
1.5%
84
 
0.9%
94
 
0.9%
ValueCountFrequency (%)
2021
0.2%
1651
0.2%
1281
0.2%
1241
0.2%
1231
0.2%
1161
0.2%
1131
0.2%
1071
0.2%
1061
0.2%
1051
0.2%

FairlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.07002188
Minimum0
Maximum660
Zeros227
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:11.778874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q316
95-th percentile46.4
Maximum660
Range660
Interquartile range (IQR)16

Descriptive statistics

Standard deviation36.20863518
Coefficient of variation (CV)2.770357656
Kurtosis224.7286191
Mean13.07002188
Median Absolute Deviation (MAD)1
Skewness13.02940846
Sum5973
Variance1311.065262
MonotonicityNot monotonic
2023-02-12T13:21:11.910487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0227
49.7%
614
 
3.1%
1612
 
2.6%
811
 
2.4%
710
 
2.2%
119
 
2.0%
129
 
2.0%
179
 
2.0%
158
 
1.8%
98
 
1.8%
Other values (52)140
30.6%
ValueCountFrequency (%)
0227
49.7%
13
 
0.7%
23
 
0.7%
33
 
0.7%
47
 
1.5%
55
 
1.1%
614
 
3.1%
710
 
2.2%
811
 
2.4%
98
 
1.8%
ValueCountFrequency (%)
6601
0.2%
1411
0.2%
1331
0.2%
1201
0.2%
1141
0.2%
1071
0.2%
1011
0.2%
991
0.2%
812
0.4%
771
0.2%

LightlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.0700219
Minimum0
Maximum720
Zeros72
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:12.090024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164
median181
Q3257
95-th percentile348.6
Maximum720
Range720
Interquartile range (IQR)193

Descriptive statistics

Standard deviation122.2053721
Coefficient of variation (CV)0.7185591605
Kurtosis0.3822726388
Mean170.0700219
Median Absolute Deviation (MAD)90
Skewness0.3525141968
Sum77722
Variance14934.15298
MonotonicityNot monotonic
2023-02-12T13:21:12.217719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072
 
15.8%
16
 
1.3%
2485
 
1.1%
2304
 
0.9%
2124
 
0.9%
2764
 
0.9%
2084
 
0.9%
1533
 
0.7%
1763
 
0.7%
2413
 
0.7%
Other values (241)349
76.4%
ValueCountFrequency (%)
072
15.8%
16
 
1.3%
23
 
0.7%
31
 
0.2%
61
 
0.2%
82
 
0.4%
91
 
0.2%
111
 
0.2%
121
 
0.2%
141
 
0.2%
ValueCountFrequency (%)
7201
0.2%
6301
0.2%
5861
0.2%
5061
0.2%
4911
0.2%
4751
0.2%
4221
0.2%
4011
0.2%
3971
0.2%
3901
0.2%

SedentaryMinutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct315
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean995.2822757
Minimum32
Maximum1440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:12.353902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile465.4
Q1728
median1057
Q31285
95-th percentile1440
Maximum1440
Range1408
Interquartile range (IQR)557

Descriptive statistics

Standard deviation337.021404
Coefficient of variation (CV)0.3386189146
Kurtosis-0.6782227116
Mean995.2822757
Median Absolute Deviation (MAD)300
Skewness-0.3655631139
Sum454844
Variance113583.4267
MonotonicityNot monotonic
2023-02-12T13:21:12.487053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144063
 
13.8%
11814
 
0.9%
14394
 
0.9%
11253
 
0.7%
11503
 
0.7%
8423
 
0.7%
11393
 
0.7%
7003
 
0.7%
10553
 
0.7%
13283
 
0.7%
Other values (305)365
79.9%
ValueCountFrequency (%)
321
0.2%
611
0.2%
751
0.2%
991
0.2%
1461
0.2%
1611
0.2%
1871
0.2%
1981
0.2%
2071
0.2%
2091
0.2%
ValueCountFrequency (%)
144063
13.8%
14394
 
0.9%
14382
 
0.4%
14321
 
0.2%
14281
 
0.2%
14141
 
0.2%
14071
 
0.2%
14062
 
0.4%
14041
 
0.2%
13991
 
0.2%

Calories
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct383
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2189.452954
Minimum0
Maximum4562
Zeros5
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-02-12T13:21:12.636686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile790.8
Q11776
median2062
Q32667
95-th percentile3716
Maximum4562
Range4562
Interquartile range (IQR)891

Descriptive statistics

Standard deviation815.4845229
Coefficient of variation (CV)0.3724603999
Kurtosis0.5200100199
Mean2189.452954
Median Absolute Deviation (MAD)422
Skewness0.2363575209
Sum1000580
Variance665015.0071
MonotonicityNot monotonic
2023-02-12T13:21:12.767339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177611
 
2.4%
18788
 
1.8%
20606
 
1.3%
18205
 
1.1%
05
 
1.1%
19205
 
1.1%
14295
 
1.1%
13244
 
0.9%
21883
 
0.7%
19353
 
0.7%
Other values (373)402
88.0%
ValueCountFrequency (%)
05
1.1%
501
 
0.2%
1821
 
0.2%
2511
 
0.2%
3992
 
0.4%
4461
 
0.2%
4891
 
0.2%
5381
 
0.2%
6001
 
0.2%
6251
 
0.2%
ValueCountFrequency (%)
45621
0.2%
45261
0.2%
44301
0.2%
42861
0.2%
42341
0.2%
42201
0.2%
41961
0.2%
41281
0.2%
40391
0.2%
40341
0.2%

Interactions

2023-02-12T13:21:06.182195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:37.443258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:39.971689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:42.012427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:44.036483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:46.035491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:48.827804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:50.861019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:52.997453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:55.258978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:57.427808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:59.545193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:01.661309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:04.042006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:06.319829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:37.598866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:40.116336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:42.161030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:44.180097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:46.190616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:48.977320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:51.012586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:53.155029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:55.392621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:57.566562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:59.695789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:01.819918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:04.196862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:06.472419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:37.744022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:40.253934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:42.307644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:44.330207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:46.340191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:49.111964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:51.164181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:53.305628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:55.549749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:57.708715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:59.852372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:01.969530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:04.343235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:06.598083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:37.884786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:40.394559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:42.454253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:44.469834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:46.477822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:49.244605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:51.313779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:53.456222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:55.681432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:57.849323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:59.989006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:02.127284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:04.486364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:06.740736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:38.038917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:40.545699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:42.589925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:44.613454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:46.628419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:49.378249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:51.454404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:53.583881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:55.825046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:57.997925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:00.138604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:02.463043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:04.632973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:06.884424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:38.285261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:40.687377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:42.740324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:44.767039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:46.795984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:49.546799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:51.609501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:53.737472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:55.972936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:58.153509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:00.290202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:02.620171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:04.788553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:07.021576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:38.576480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:40.813804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:42.883902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:44.902677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:46.948562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:49.685430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:51.766016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:53.872726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:56.130516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:58.297127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:00.427832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:02.766772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:04.930176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:07.161203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:38.927540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:40.968390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:43.022530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:45.039312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:47.108702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:49.835028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:51.917567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:54.027314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:56.307045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:58.445728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:00.578431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:02.937339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:05.101718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:07.314837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:39.082673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:41.111136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:43.164150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:45.175945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:47.263299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:49.972659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:52.076179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:54.197036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:56.472599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:58.609869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:00.743672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:03.095891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:05.245332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:07.450522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:39.233498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:41.247878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:43.318738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:45.316573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:48.088235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:50.132267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:52.219986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:54.522946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:56.635168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:58.768732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:00.911354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:03.262448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:05.411324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:07.618061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:39.384995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:41.401061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:43.469336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:45.484161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:48.244817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:50.278258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:52.387053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:54.673544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:56.795738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:58.923742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:01.080861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:03.432472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:05.565511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:07.748712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:39.511919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:41.549664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:43.620482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:45.625175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:48.397445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:50.418882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:52.541924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:54.820152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:56.950334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:59.086307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:01.231460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:03.582585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:05.716108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:07.894324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:39.659523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:41.703254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:43.752634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:45.757039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:48.547544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:50.577196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:52.695293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:54.971745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:57.117883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:59.243926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:01.378066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:03.732215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:05.875016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:08.029962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:39.813115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:41.864822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:43.895298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:45.898922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:48.705130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:50.709395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:52.852837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:55.110376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:57.276132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:20:59.399075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:01.519688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:03.888795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-12T13:21:06.028605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-12T13:21:12.896957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-12T13:21:13.151310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-12T13:21:13.381272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-12T13:21:13.611822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-12T13:21:08.263334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-12T13:21:08.557644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
015039603663/25/2016110047.117.110.02.570.464.070.033122058041819
115039603663/26/20161760911.5511.550.06.920.733.910.089172745882154
215039603663/27/2016127368.538.530.04.660.163.710.05652686051944
315039603663/28/2016132318.938.930.03.190.794.950.0392022410801932
415039603663/29/2016120417.857.850.02.161.094.610.028282437631886
515039603663/30/2016109707.167.160.02.360.514.290.0301322311741820
615039603663/31/2016122567.867.860.02.290.495.040.033122398201889
715039603664/1/2016122627.877.870.03.320.833.640.047212008661868
815039603664/2/2016112487.257.250.03.000.453.740.040112446361843
915039603664/3/2016100166.376.370.00.911.284.180.015303146551850

Last rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
44788776893914/3/2016152608.1900008.1900000.01.800.755.570.001061725910583864
44888776893914/4/20162077918.41000018.4100000.011.730.656.000.00781620811383662
44988776893914/5/2016106958.1200008.1200000.00.770.187.090.0110324611812834
45088776893914/6/20162413620.91000020.9100000.012.220.548.080.00871631810194039
45188776893914/7/2016109108.4200008.4200000.02.960.395.030.00321121211852947
45288776893914/8/20162301420.38999920.3899990.011.100.638.620.0070293599824196
45388776893914/9/2016164708.0700008.0700000.00.000.028.020.0090928910523841
45488776893914/10/20162849727.53000127.5300010.021.921.124.460.001284621110554526
45588776893914/11/2016106228.0600008.0600000.01.470.156.370.0118722511902820
45688776893914/12/201623501.7800001.7800000.00.000.001.780.000058531938